The problem of selecting the optimum system of models for forecasting short-term railway traffic volumes is considered. The historical data is the daily volume of railway traffic between pairs of stations for different types of cargo. The given time series are highly volatile, noisy, and nonstationary. A system is proposed that selects the optimum superpositioning of forecasting models with respect to features of the historical data. A model of sliding averages, exponential and kernel-smoothing models, the ARIMA model, Croston’s method, and LSTM neural networks are considered as candidates for inclusion in superpositioning.

中文翻译：

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选择铁路货运预测模型的叠加

考虑了选择用于预测短期铁路交通量的最佳模型系统的问题。历史数据是不同类型的货物在成对的车站之间每天的铁路运输量。给定的时间序列非常不稳定，嘈杂且不稳定。提出了一种针对历史数据的特征选择最佳预测模型叠加的系统。滑动平均模型，指数模型和核平滑模型，ARIMA模型，Croston方法和LSTM神经网络被认为是包含在叠加中的候选者。